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raghavan.ppt

  1. 1. NeurOn: Modeling Ontology for Neurosurgery K. S. Raghavan & C. Sajana Indian Statistical Institute Bangalore
  2. 2. Information & Healthcare <ul><li>Health care is a knowledge intensive activity; Available knowledge is a fluid mix of: </li></ul><ul><ul><li>Scholarly documents </li></ul></ul><ul><ul><li>New experiences </li></ul></ul><ul><ul><li>Contextual information </li></ul></ul><ul><ul><li>Expert insights </li></ul></ul><ul><li>collectively providing a framework for decision-making </li></ul>
  3. 3. Information & Healthcare <ul><li>Patient records as an important source of valuable information </li></ul><ul><ul><li>Much of this valuable information may not even appear in published sources or become a part of standard texts until years later </li></ul></ul><ul><li>Quality of health care vis-à-vis access to patient records with defined similarities to the problem on hand </li></ul>
  4. 4. Decision-making is… something which concerns all of us, both as makers of the choice and as sufferers of the consequences. -D. Lindley K. S. Raghavan, Indian Statistical Institute, Bangalore, India
  5. 5. Background <ul><li>The work reported here is set in a large hospital (and is in progress) </li></ul><ul><ul><li>Present system & its limitations </li></ul></ul><ul><li>WINISIS database with hyperlinks to related files (Such as X-rays, CT Scans, Pathology reports, etc) </li></ul><ul><ul><li>Data on a large number of parameters </li></ul></ul><ul><ul><li>The range of relations between concepts in a complex domain such as health care </li></ul></ul><ul><ul><li>Search technologies in thesauri – based IR systems </li></ul></ul>
  6. 6. The Present Study <ul><li>Hypotheses </li></ul><ul><ul><li>Ontologies can help build more effective information support systems in healthcare. </li></ul></ul><ul><ul><li>Ontologies can support the need of the healthcare and delivery process to transmit, re-use and share patient data </li></ul></ul><ul><li>Why Ontology? </li></ul><ul><ul><li>Ontologies are effective in representing domain knowledge </li></ul></ul><ul><ul><li>Possible to include ‘IF – THEN’ rules to support inferencing </li></ul></ul>
  7. 7. Ontology? <ul><li>Gruber’s Definition: “Explicit Specification of a Conceptualization” </li></ul><ul><li>Studer’s extension: “ a formal, explicit specification of a shared conceptualization” </li></ul><ul><li>For practical purposes and applications a domain ontology could be perceived as: </li></ul><ul><ul><li>The complete set of domain concepts and their interrelationships </li></ul></ul>
  8. 8. Ontology? <ul><li>A semantic network of concepts grouped into classes and subclasses and linked by means of a well defined set of relations. Ontologies also have a set of rules that support inferencing </li></ul>
  9. 9. The Project <ul><li>About 1500 patient records of the Neurosurgery unit of a large hospital </li></ul><ul><ul><li>The neurological disorder </li></ul></ul><ul><ul><ul><li>disease name (Final Diagnosis) </li></ul></ul></ul><ul><ul><li>specific treatment </li></ul></ul><ul><ul><li>symptoms </li></ul></ul><ul><ul><li>associated illnesses </li></ul></ul><ul><ul><li>Patient Data; name, ID number, doctor’s name, disease index number, gender, age, age range, consciousness level, visual acuity details , etc </li></ul></ul>
  10. 10. The central theme of every patient record ran more or less like this: A Patient Has Neurological Disorder that is Diagnosed and Treated Using Method of Treatment Leading to Result
  11. 11. Domain Concepts <ul><li>Patient Records contained four broad types of data: </li></ul><ul><li>These could be categorized under the 3 top level categories of Ranganathan, viz., Personality [P], Matter Property [MP] and Energy [E] </li></ul>Patient-Related data Medicine-Related Concepts Healthcare Personnel Related data Institution-Related data
  12. 13. Queries <ul><li>In building the ontology it was important to have some idea of the nature of queries that the system should respond to: </li></ul><ul><ul><li>Identify </li></ul></ul><ul><ul><ul><li>Patients above 40 years of age and suffering from Astrocytoma </li></ul></ul></ul><ul><ul><ul><li>Patients with brain diseases having symptoms of headache and visual impairment </li></ul></ul></ul><ul><ul><ul><li>Records of patients who were administered drug XXX and had post – surgery complication of vision loss </li></ul></ul></ul>
  13. 14. Queries <ul><li>While a clear picture will emerge only after the system is implemented; </li></ul><ul><ul><li>A small query library was built to serve as the basis for defining classes and sub-classes </li></ul></ul>
  14. 15. The Ontology <ul><li>No one perfect way of building an ontology </li></ul><ul><ul><li>Definition of classes, properties, etc. </li></ul></ul><ul><ul><ul><li>decisions regarding how detailed the classes and / or relations should be is largely based on the purpose and ease of maintenance </li></ul></ul></ul><ul><ul><ul><ul><li>’ female patients ’ could be seen as a subclass of ‘patients ’; it could also be handled as: Patient HAS GENDER and by fdefining acceptable values for gender </li></ul></ul></ul></ul>
  15. 16. The Ontology <ul><li>The decision-support system proposed here is conceived to have at least two major components when completed: </li></ul><ul><ul><li>The ontology as part of the search interface; and </li></ul></ul><ul><ul><li>A database of patient records linked to related records in other databases, e.g. of images (scans, X-rays, etc) </li></ul></ul>
  16. 17. Populating the Ontology <ul><li>Phase 1 </li></ul><ul><ul><li>Medicine-related concepts - neurological disorders including associated symptoms and characteristics and their treatment – </li></ul></ul><ul><ul><li>patient data. </li></ul></ul><ul><ul><li>Other sub-domain concepts will be added later </li></ul></ul><ul><li>Complex nature of relations </li></ul>
  17. 19. Populating the Ontology <ul><li>Structuring the terms into a hierarchy </li></ul><ul><ul><li>Inconsistencies in terminology used by hospital’s health-care personnel </li></ul></ul><ul><li>SNOMED CT was used to standardize the terminology </li></ul><ul><ul><li>Corresponding MeSH Terms & terms used by hospital personnel built in as relations: </li></ul></ul><ul><ul><ul><ul><li>[SNOMED CT for Domain Concept] <has physician’s term> [Term used in the patient record] </li></ul></ul></ul></ul>
  18. 20. Populating the Ontology <ul><li>Properties </li></ul><ul><ul><li>Object </li></ul></ul><ul><ul><li>Data type </li></ul></ul><ul><li>A general idea of the class hierarchy and properties (see Figure) </li></ul>
  19. 22. Future work <ul><li>To be implemented in the hospital </li></ul><ul><ul><li>Only a limited number of patient records used </li></ul></ul><ul><ul><li>Tested with a few sample queries </li></ul></ul><ul><li>To include concepts related to healthcare institutions and personnel </li></ul><ul><li>To link relevant manuals, reference sources, text books and papers with a view to widen the knowledge base available to the users of the decision support system </li></ul>
  20. 23. Future work <ul><li>To build rules for inferencing that allow </li></ul><ul><ul><li>reasonable and intelligent guess of the probable cause or condition of a patient based on values of certain clinical parameters </li></ul></ul><ul><ul><li>decision regarding the possible course of action </li></ul></ul><ul><ul><li>defining parameters that could generate an alert message </li></ul></ul>
  21. 24. Future work <ul><li>The complexity of medical terminology; </li></ul><ul><ul><li>Non-availability of exact equivalents for some of the terms used (in patient records) in the standard medical terminologies (like MeSH and SNOMED CT) </li></ul></ul><ul><ul><li>Ethical issues </li></ul></ul><ul><li>Can we define principles for decisions regarding ‘ classes ’ ‘ subclasses ’ ‘ properties ’ and ‘ relation types ’ for ontology? </li></ul>

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